Distributionally robust optimization scheduling of port energy system considering hydrogen production and ammonia synthesis

被引:5
|
作者
Liu, Xiaoou [1 ]
机构
[1] China Power Engn Consulting Grp CO LTD, Ande Rd 65, Beijing 100120, Peoples R China
关键词
Port energy system; Green hydrogen; Ammonia synthesis; Wasserstein distance; Distributionally robust optimization; MANAGEMENT; FRAMEWORK;
D O I
10.1016/j.heliyon.2024.e27615
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In order to effectively address the uncertainty risks of port energy system caused by intermittence and fluctuation of renewable energy, this paper proposes a scheduling method for port energy system based on distributionally robust optimization (DRO) considering ammonia synthesis after hydrogen production by water electrolysis (P2H2A), and uses real data from Tianjin Port for example analysis. The calculation results show that 1 h selected for the scheduling interval of P2H2A is reasonable, it can ensure that the ammonia synthesis reaction transitions smoothly to the new steady state, and the temperature and pressure of the ammonia converter meet safety constraints. The two-stage scheduling of port energy system based on DRO can be divided into pre-scheduling in the day-ahead stage and rescheduling in the intraday stage, which can improve the capacity of anti-risk for stochastic optimization and overcome the conservatism of robust optimization, and consider economy and robustness. Moreover, the rescheduling decision can be transformed to a prediction error function, the result of two-stage scheduling based on DRO is the pre-scheduling result, which is between the cost of stochastic optimization and robust optimization. As the Wasserstein distance-based sphere radius increases, the pre-scheduling cost of DRO gradually deviates from risk neutral stochastic optimization and leans towards risk averse robust optimization. When the Wasserstein distance-based sphere radius remains constant, the variance gradually decreases as the number of scenarios increases, which can promote the Wasserstein distance-based fuzzy set to converge to the true distribution. When the number of scenarios is greater than 15, the pre-scheduling cost will no longer fluctuate significantly, and the calculation time is in the range of 1200 s-6600 s. It can meet the demands of day-ahead scheduling calculation time. Therefore, the scheduling model has outstanding advantages in the computing time to improve the flexibility and economy of Tianjin Port's energy system scheduling, considering ammonia synthesis after hydrogen production using renewable energy.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] Distributionally Robust Co-Optimization of Energy and Reserve Dispatch
    Wei, Wei
    Liu, Feng
    Mei, Shengwei
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2016, 7 (01) : 289 - 300
  • [42] Robust multi-objective optimization for energy production scheduling in microgrids
    Wang, Luhao
    Li, Qiqiang
    Zhang, Bingying
    Ding, Ran
    Sun, Mingshun
    ENGINEERING OPTIMIZATION, 2019, 51 (02) : 332 - 351
  • [43] Multi-Time-Scale Robust Optimization Strategy for Integrated Energy System Considering the Refinement of Hydrogen Energy Use
    Hu J.
    Tong Y.
    Liu X.
    Wang J.
    Xu Y.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2024, 39 (05): : 1419 - 1435
  • [44] Distributionally robust optimal dispatching of integrated energy system considering line pack effect of gas network
    Yi W.
    Bu Q.
    Lu S.
    Qin Y.
    Li P.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2022, 42 (06): : 53 - 60and83
  • [45] Distributionally robust optimal configuration of battery energy storage system considering nodal RoCoF security constraints
    Xu, Danyang
    Wu, Zhigang
    Guan, Lin
    JOURNAL OF ENERGY STORAGE, 2024, 104
  • [46] Distributionally robust scheduling with CVaR assessment for reconfigurable distribution networks hosting renewable energy penetrations
    Yang, Linfeng
    Liu, Xiangyu
    Yin, Jinjiao
    Zheng, Haiyan
    ELECTRIC POWER SYSTEMS RESEARCH, 2025, 242
  • [47] Distributionally robust optimization configuration method for island microgrid considering extreme scenarios
    Zhang, Qingzhu
    Mu, Yunfei
    Jia, Hongjie
    Yu, Xiaodan
    Hou, Kai
    ENERGY AND AI, 2024, 17
  • [48] Distributionally Robust Co-Optimization of Energy and Reserve Dispatch of Integrated Electricity and Heat System
    Skalyga, Mikhail
    Wu, Quiwei
    2020 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2020,
  • [49] Collaborative optimization scheduling of integrated energy system considering user dissatisfaction
    Ma, Kai
    Zhang, Rencai
    Yang, Jie
    Song, Debao
    ENERGY, 2023, 274
  • [50] A data-driven scheduling model of virtual power plant using Wasserstein distributionally robust optimization
    Liu, Huichuan
    Qiu, Jing
    Zhao, Junhua
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 137